Scholarlink

<p>Vehicle-related flood fatalities account for a majority of flooding deaths in the United States. As floods become more frequent and severe, emergency operators need accurate early warning systems to enact road closures and dispatch first responders. We present an operational flood forecasting framework that connects large-scale hydrologic predictions with site-level transportation impacts. This system integrates NOAA’s National Water Model (NWM) with a new data assimilation framework based on Kalman Filtering to generate improved discharge and stage predictions at progressive 12-hour forecast horizons. These discharge and stage forecasts are joined with a large-scale bridge infrastructure database to generate site-level probabilistic flood warnings. Tested across two major river basins in Texas, our data assimilation and forecasting framework outperforms the NWM’s existing nudging method at predicting bridge flooding impacts over all lead times considered. By enabling accurate site-level bridge warnings, the proposed framework will enable more targeted management of transportation systems during floods.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Flood early warning system with data assimilation enables site-level forecasting of bridge impacts

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月17日

查看全文
  • Jeil Oh,
  • Matthew Bartos

摘要

Vehicle-related flood fatalities account for a majority of flooding deaths in the United States. As floods become more frequent and severe, emergency operators need accurate early warning systems to enact road closures and dispatch first responders. We present an operational flood forecasting framework that connects large-scale hydrologic predictions with site-level transportation impacts. This system integrates NOAA’s National Water Model (NWM) with a new data assimilation framework based on Kalman Filtering to generate improved discharge and stage predictions at progressive 12-hour forecast horizons. These discharge and stage forecasts are joined with a large-scale bridge infrastructure database to generate site-level probabilistic flood warnings. Tested across two major river basins in Texas, our data assimilation and forecasting framework outperforms the NWM’s existing nudging method at predicting bridge flooding impacts over all lead times considered. By enabling accurate site-level bridge warnings, the proposed framework will enable more targeted management of transportation systems during floods.